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Neuropsychiatric and neurodegenerative disorders exhibit cell–type–specific characteristics1–8, yet most transcriptome–wide association studies have been constrained by the use of homogenate brain tissue9–11, limiting their resolution and power. Here, we present a single–nucleus transcriptome–wide association study (snTWAS) leveraging single–nucleus RNA sequencing of over 6 million nuclei from the dorsolateral prefrontal cortex of 1,494 donors across three ancestries–European, African, and Admixed American. We constructed ancestry–specific single–nucleus–derived transcriptomic imputation models (snTIMs) including up to 27 non–overlapping cellular populations, enhancing the resolution of genetically regulated gene expression (GReX) in the brain and uncovering novel gene–trait associations across 12 neuropsychiatric and neurodegenerative traits. Our snTWAS framework revealed cell–type–specific dysregulation of GReX, identifying over 4,000 novel gene–trait associations not detected by bulk tissue approaches. By applying these snTIMs to the Million Veteran Program, we validated major findings and explored the pleiotropy of cell–type–specific GReX, revealing cross–ancestry concordance and fine–mapping causal genes. This approach enhances the discovery of biologically relevant pathways and gene targets, highlighting the importance of cell–type resolution and ancestry–specific models in understanding the genetic architecture of complex brain disorders.
Neuropsychiatric and neurodegenerative disorders exhibit cell–type–specific characteristics1–8, yet most transcriptome–wide association studies have been constrained by the use of homogenate brain tissue9–11, limiting their resolution and power. Here, we present a single–nucleus transcriptome–wide association study (snTWAS) leveraging single–nucleus RNA sequencing of over 6 million nuclei from the dorsolateral prefrontal cortex of 1,494 donors across three ancestries–European, African, and Admixed American. We constructed ancestry–specific single–nucleus–derived transcriptomic imputation models (snTIMs) including up to 27 non–overlapping cellular populations, enhancing the resolution of genetically regulated gene expression (GReX) in the brain and uncovering novel gene–trait associations across 12 neuropsychiatric and neurodegenerative traits. Our snTWAS framework revealed cell–type–specific dysregulation of GReX, identifying over 4,000 novel gene–trait associations not detected by bulk tissue approaches. By applying these snTIMs to the Million Veteran Program, we validated major findings and explored the pleiotropy of cell–type–specific GReX, revealing cross–ancestry concordance and fine–mapping causal genes. This approach enhances the discovery of biologically relevant pathways and gene targets, highlighting the importance of cell–type resolution and ancestry–specific models in understanding the genetic architecture of complex brain disorders.
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